1.AbstractMillions of Americans suffer from illnesses with non-existent or ineffective drug treatment. Identifying plausible drug candidates is a major barrier to drug development due to the large amount of time and resources required; approval can take years when people are suffering now. While computational tools can expedite drug candidate discovery, these tools typically require programming expertise that many biologists lack. Though biomedical databases continue to grow, they have proven difficult to integrate and maintain, and non-programming interfaces for these data sources are scarce and limited in capability. This creates an opportunity for us to present a suite of user-friendly software tools to aid computational discovery of novel treatments through de novo discovery or repurposing. Our tools eliminate the need for researchers to acquire computational expertise by integrating multiple databases and offering an intuitive graphical interface for analyzing these publicly available data. We built a computational knowledge graph focused on biomedical concepts related to drug discovery, designed visualization tools that allow users to explore complex relationships among entities in the graph, and served these tools through a free and user-friendly web interface. We show that users can conduct complex analyses with relative ease and that our knowledge graph and algorithms recover approved repurposed drugs. Our evaluation indicates that our method provides an intuitive, easy, and effective toolkit for discovering drug candidates. We show that our toolkit makes computational analysis for drug development more accessible and efficient and ultimately plays a role in bringing effective treatments to all patients.Our application is hosted at: https://biomedical-graph-visualizer.wl.r.appspot.com/
Globally, noise exposure from occupational and nonoccupational sources is common, and, as a result, noise-induced hearing loss affects tens of millions of people. Occupational noise exposures have been studied and regulated for decades, but nonoccupational sound exposures are not well understood. The nationwide Apple Hearing Study, launched using the Apple research app in November 2019 (Apple Inc., Cupertino, CA), is characterizing the levels at which participants listen to headphone audio content, as well as their listening habits. This paper describes the methods of the study, which collects data from several types of hearing tests and uses the Apple Watch noise app to measure environmental sound levels and cardiovascular metrics. Participants, all of whom have consented to participate and share their data, have already contributed nearly 300 × 106 h of sound measurements and 200 000 hearing assessments. The preliminary results indicate that environmental sound levels have been higher, on average, than headphone audio, about 10% of the participants have a diagnosed hearing loss, and nearly 20% of the participants have hearing difficulty. The study’s analyses will promote understanding of the overall exposures to sound and associated impacts on hearing and cardiovascular health. This study also demonstrates the feasibility of collecting clinically relevant exposure and health data outside of traditional research settings.
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